Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review
}
TY - GEN
T1 - Nonlinear identification of a gas turbine system in transient operation mode using neural network
AU - Rahnama, M.
AU - Ghorbani, H.
AU - Montazeri, Allahyar
PY - 2012
Y1 - 2012
N2 - In this paper ANN (Artificial Neural Network) identification techniques are developed to estimate a General Electric frame 9, 116MW combined cycle, single shaft heavy duty gas turbine dynamic behaviors during loading process based on available operational data in Montazer Ghaem power plant in Karaj. Related Input and output data are chosen based on thermodynamics and first order linear models. Electrical power and exhaust gas temperature are chosen as system main outputs which can be expressed by fuel flow, shaft speed and compressor inlet guide vanes considering the ambient temperature effects. The operating condition of the gas turbine during identification procedure is considered from full speed no load to full load. Comprehensive results perform that this model outputs is closer to the experimental data than conventional NARX models and can predict system behaviors perfectly.
AB - In this paper ANN (Artificial Neural Network) identification techniques are developed to estimate a General Electric frame 9, 116MW combined cycle, single shaft heavy duty gas turbine dynamic behaviors during loading process based on available operational data in Montazer Ghaem power plant in Karaj. Related Input and output data are chosen based on thermodynamics and first order linear models. Electrical power and exhaust gas temperature are chosen as system main outputs which can be expressed by fuel flow, shaft speed and compressor inlet guide vanes considering the ambient temperature effects. The operating condition of the gas turbine during identification procedure is considered from full speed no load to full load. Comprehensive results perform that this model outputs is closer to the experimental data than conventional NARX models and can predict system behaviors perfectly.
M3 - Conference contribution/Paper
SN - 978-1-4673-4844-7
SP - 1
EP - 6
BT - Thermal Power Plants (CTPP), 2012 4th Conference on
PB - IEEE
T2 - 2012 4th Conference on Thermal Power Plants (CTPP)
Y2 - 18 December 2012 through 19 December 2012
ER -